问财量化选股策略逻辑
选股逻辑：选择换手率在3%到12%之间，涨跌幅乘以超大单净量大于0，且开盘价在十日线左右的股票为选股范围。
选股逻辑分析
该选股逻辑综合考虑了股票的交易活跃程度、市场情绪和热点、股价走势等因素，特别是采用了开盘价在十日线左右的条件来关注股票的技术指标，有助于避免投资盲目性和过度依赖市场走势等情况。
有何风险？
可能会漏掉一些短期市场趋势较弱但未来发展潜力较大的股票，同时对技术指标的选取也可能存在较大主观性和滞后性，过于关注短期涨跌幅等表象而忽略股票时间价值等因素。另外，对于换手率阈值和开盘价与十日线位置的要求也可能较为严格，降低了选股池中股票的数量和多样性。
如何优化？
可以加入更多技术指标、基本面数据、市场预测数据等来评估股票的价值潜力和未来发展趋势，优化换手率、开盘价和十日线等具体指标的设定，降低筛选条件和门槛来增加选股池中股票的数量和多样性，注重对股票发展周期、行业情况、资本市场政策等方面的综合评估和预测等。
最终的选股逻辑
选择换手率在3%到12%之间，涨跌幅乘以超大单净量大于0，且开盘价在十日线左右的股票为选股范围。
同花顺指标公式代码参考
以下是同花顺指标所需公式：
选股公式：
-- 计算涨跌幅乘以超大单净量
SuperVolume: (C*Big)/10000;
-- 计算MA10和开盘价
MA10:MA(CLOSE,10);
Topen:OPEN/MA10;
-- 计算选股
SELECT STOCK_SYMBOL FROM (
SELECT STOCK_SYMBOL AS code, (C1 / C0) * SuperVolume AS Score FROM
(
SELECT STOCK_SYMBOL AS code, CLOSE AS C0, OPEN AS O FROM CandlesMin WHERE Cdl[:1] = LAST AND TIME = [TIME-1]
) ST,
(
SELECT STOCK_SYMBOL AS code, CLOSE AS C1,NetChangeRatio AS Chg FROM CandlesMin WHERE Cdl[:1] = LAST AND TIME = [TIME-0]
) MT,
(
SELECT STOCK_SYMBOL AS code, VOL AS Vol FROM CandlesMin WHERE Cdl[:1] = LAST AND TIME = [TIME-0]
) VT,
(
SELECT STOCK_SYMBOL AS code, BUY_VOL_L_VOL AS Big FROM CandlesDay WHERE Cdl[:1] = LAST AND TIME = [TIME-1]
) BT
WHERE ST.code = MT.code AND MT.code = VT.code AND VT.code = BT.code AND MT.Chg > 2 AND VT.VOL_AVG_5DAY > 2000000 AND ST.O / MA10 >= 0.98 AND ST.O / MA10 <= 1.02 AND VOL >= 1000000 AND Score>0 AND C1>=5
ORDER BY Score DESC
LIMIT 10
Python代码参考
以下是Python代码实现该选股逻辑：
import pandas as pd
from typing import List
from datetime import datetime, timedelta
import talib
def select_stock(data: pd.DataFrame, n=10) -> List[str]:
selected_stocks = []
for code, df in data.groupby(level=0):
df = df.sort_values('trade_time', ascending=True)
ma10 = talib.MA(df['close'], timeperiod=10)
if df['dt'].iloc[-1] and \
(df['float_shares'].iloc[-1] / 1000000000 <= 5.5) and \
(df['volume'].iloc[-1] / df['volume'].iloc[-6:-1].mean() > 3) and \
(df['turnover_rate'].iloc[-1] > 3) and (df['turnover_rate'].iloc[-1] < 12) and \
(df['pct_chg'].iloc[-1] * (df['buy_amount'].iloc[-1] - df['sell_amount'].iloc[-1]) / 10000 > 0) and \
(df['open'].iloc[-1] / ma10[-1] >= 0.98) and (df['open'].iloc[-1] / ma10[-1] <= 1.02):
s_weight = df['turnover_rate'].mean() * df['volume'].mean() / (df['close'].iloc[-1] * 10000)
selected_stocks.append((code, s_weight))
selected_stocks.sort(key=lambda x: x[1], reverse=True)
selected_stocks = selected_stocks[:n]
return [x[0] for x in selected_stocks]
## 如何进行量化策略实盘？
请把您优化好的选股语句放入文章最下面模板的选股语句中即可。
select_sentence = '市值小于100亿' #选股语句。
模板如何使用？
点击图标右上方的复制按钮，复制到自己的账户即可使用模板进行回测。
## 如果有任何问题请添加 下方的二维码进群提问。
